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Preparing sparse solvers for exascale computing
Sparse solvers provide essential functionality for a wide variety of scientific applications.
Highly parallel sparse solvers are essential for continuing advances in high-fidelity, multi …
Highly parallel sparse solvers are essential for continuing advances in high-fidelity, multi …
Nonsymmetric Algebraic Multigrid Based on Local Approximate Ideal Restriction (AIR)
Algebraic multigrid (AMG) solvers and preconditioners are some of the fastest numerical
methods to solve linear systems, particularly in a parallel environment, scaling to hundreds …
methods to solve linear systems, particularly in a parallel environment, scaling to hundreds …
Nonsymmetric reduction-based algebraic multigrid
Algebraic multigrid (AMG) is often an effective solver for symmetric positive definite (SPD)
linear systems resulting from the discretization of general elliptic PDEs or the spatial …
linear systems resulting from the discretization of general elliptic PDEs or the spatial …
FFT, FMM, and multigrid on the road to exascale: Performance challenges and opportunities
FFT, FMM, and multigrid methods are widely used fast and highly scalable solvers for elliptic
PDEs. However, emerging large-scale computing systems are introducing challenges in …
PDEs. However, emerging large-scale computing systems are introducing challenges in …
Reducing communication in algebraic multigrid with multi-step node aware communication
Algebraic multigrid (AMG) is often viewed as a scalable O (n) solver for sparse linear
systems. Yet, AMG lacks parallel scalability due to increasingly large costs associated with …
systems. Yet, AMG lacks parallel scalability due to increasingly large costs associated with …
Amgt: Algebraic multigrid solver on tensor cores
Algebraic multigrid (AMG) methods are particularly efficient to solve a wide range of sparse
linear systems, due to their good flexibility and adaptability. Even though modern parallel …
linear systems, due to their good flexibility and adaptability. Even though modern parallel …
αSetup-AMG: an adaptive-setup-based parallel AMG solver for sequence of sparse linear systems
The algebraic multigrain (AMG) is one of the most frequently used algorithms for the solution
of large-scale sparse linear systems in many realistic simulations of science and …
of large-scale sparse linear systems in many realistic simulations of science and …
Node aware sparse matrix–vector multiplication
The sparse matrix–vector multiply (SpMV) operation is a key computational kernel in many
simulations and linear solvers. The large communication requirements associated with a …
simulations and linear solvers. The large communication requirements associated with a …
A comparison of classical and aggregation-based algebraic multigrid preconditioners for high-fidelity simulation of wind turbine incompressible flows
This paper presents a comparison of parallel strong scaling performance of classical and
aggregation algebraic multigrid (AMG) preconditioners in the context of wind turbine …
aggregation algebraic multigrid (AMG) preconditioners in the context of wind turbine …
A two-scale approach for efficient on-the-fly operator assembly in massively parallel high performance multigrid codes
Large scale matrix-free finite element implementations save memory and are often
significantly faster than implementations using classical sparse matrix techniques. They are …
significantly faster than implementations using classical sparse matrix techniques. They are …